A diagnostic control method for energy-saving operation of industrial air-conditioning systems

Abstract

The present invention discloses a diagnostic control method for energy-saving operation of an industrial air-conditioning system, comprising the following steps: Step 1: Acquire the air quality parameters in the process of operation of the industrial air-conditioning system in the historical database, and conduct in-depth analysis, use the cluster analysis algorithm to divide the operating conditions of the industrial air-conditioning system, and obtain the operating condition category; Step 2: Based on the divided operating condition partitions of the industrial air-conditioning, monitor the air quality parameters in real time, and calculate the distance value between the real-time monitoring data and the cluster center of each condition. The present invention can divide and monitor the operating conditions of the industrial air-conditioning system in real time, which helps to automatically adjust the operating parameters according to the current conditions to achieve the goals of energy saving and environmental protection. By deeply analyzing the historical data and the real-time monitoring data, the method can accurately reflect the operating status and air quality characteristics of the air-conditioning system under different conditions, and provide strong support for the system's optimized control and energy-saving operation.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial air-conditioning systems, and in particular to a diagnosis and control method for energy-saving operation of industrial air-conditioning systems. Background Art

[0002] Industrial air conditioning refers to air conditioning equipment that provides environmental temperature, humidity, and cleanliness guarantees for the reliable operation of industrial product production processes or industrial process equipment. For example, constant temperature and humidity air conditioners used to ensure the normal operation of equipment in electrical control rooms, special air conditioners for high-temperature environments used in mobile bridge cranes in steelmaking workshops, and low-temperature unit air conditioners used in meat processing workshops, etc., all belong to industrial air conditioning. Industrial air conditioning uses the water circulation system in the machine to absorb the heat in the air blown in by the fan to achieve the effect of cooling. The installation scope includes air conditioner host, pipelines, insulation, valves, pump groups, control, electrical and other aspects;

[0003] In some industrial environments, such as pharmaceuticals and electronic manufacturing, there are strict standards for air quality (including particulate matter, microorganisms, volatile organic compounds, etc.). Industrial air-conditioning systems must be able to effectively filter and treat air. However, the operation and management of traditional industrial air-conditioning systems mainly rely on manual experience to set and adjust parameters, and lack real-time monitoring and analysis of system operating status and air quality under different working conditions. This not only leads to high system energy consumption, but may also affect the stability of the production environment and the health of workers due to the inability to detect and deal with abnormal situations in a timely manner. In recent years, although some advanced control systems have begun to introduce automated monitoring and adjustment technologies, these systems often focus on the monitoring of a single parameter or simple threshold control, and lack in-depth analysis and comprehensive diagnostic capabilities of the overall operating conditions of the air-conditioning system. Summary of the invention

[0004] An object of the present invention is to provide a diagnostic control method for energy-saving operation of an industrial air-conditioning system to solve at least one of the above-mentioned problems of the prior art.

[0005] The present invention provides a diagnostic control method for energy-saving operation of an industrial air-conditioning system, comprising the following steps:

[0006] Step 1: Obtain the air quality parameters during the operation of the industrial air-conditioning system in the historical database, conduct in-depth analysis, and use the cluster analysis algorithm to divide the operating conditions of the industrial air-conditioning system to obtain the operating condition category;

[0007] Step 2: Based on the divided operating condition zones of industrial air conditioners, the air quality parameters are monitored in real time. By calculating the distance between the real-time monitoring data and the cluster center of each operating condition, the operating condition category of the air conditioning system is identified and the real-time operating condition is obtained.

[0008] Step 3: Based on the real-time operating conditions obtained, continuously track the air quality status and its changing trend over time, and identify output abnormal signals;

[0009] Step 4: Based on the abnormal signal, identify the specific abnormal air quality parameters, and optimize and adjust the industrial air conditioning operating parameters in combination with the real-time operating conditions.

[0010] Beneficial effects of the present invention:

[0011] The present invention can divide and monitor the operating conditions of industrial air-conditioning systems in real time, which helps to automatically adjust the operating parameters according to the current working conditions to achieve the goals of energy saving and environmental protection. By deeply analyzing historical data and real-time monitoring data, the method can accurately reflect the operating status and air quality characteristics of the air-conditioning system under different conditions, providing strong support for the optimized control and energy-saving operation of the system.

[0012] The present invention can accurately identify the operating status of the industrial air-conditioning system, can timely discover abnormal conditions in the operation of the industrial air-conditioning system, improve the fault detection capability, and also provide a reliable basis for timely taking energy-saving control measures, which helps to ensure the stable operation and energy-saving effect of the system;

[0013] The present invention can quickly detect air quality abnormalities and effectively optimize the operating parameters of industrial air conditioners, especially the air supply volume, through calculation and adjustment strategies to improve the air quality in a targeted manner. At the same time, by comprehensively considering the correlation between the air supply volume and multiple control objectives, the scientificity and effectiveness of the adjustment strategy are ensured, thereby improving the efficiency of air quality management, and also contributing to energy conservation and emission reduction, and reducing operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 It is a flow chart of a diagnostic control method for energy-saving operation of an industrial air-conditioning system according to the present invention;

[0016] Figure 2 It is a structural schematic diagram of a diagnostic control system for energy-saving operation of an industrial air-conditioning system according to the present invention;

[0017] Figure 3 The present invention is a structural schematic diagram of a diagnostic control device for energy-saving operation of an industrial air-conditioning system.

[0018] In the figure: 3, computer device; 301, processor; 302, memory; 303, computer program; DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention. Embodiment 1

[0020] Figure 1 A flowchart of a diagnostic control method for energy-saving operation of an industrial air-conditioning system provided in Embodiment 1 of the present invention. The embodiment of the present invention can be applied to the diagnosis and control of air quality parameters during the energy-saving operation of an industrial air-conditioning system. The diagnostic control method for energy-saving operation of an industrial air-conditioning system can be executed by a diagnostic control system for energy-saving operation of an industrial air-conditioning system. The diagnostic control system for energy-saving operation of an industrial air-conditioning system can be implemented by software and / or hardware. The diagnostic control system for energy-saving operation of an industrial air-conditioning system can be configured in a diagnostic control device for energy-saving operation of an industrial air-conditioning system. Optionally, a diagnostic control device for energy-saving operation of an industrial air-conditioning system can be an electronic device, which can be a notebook, a desktop computer, a smart tablet, etc., and the embodiment of the present invention does not limit this.

[0021] like Figure 1 As shown, an embodiment of the present invention provides a diagnostic control method for energy-saving operation of an industrial air-conditioning system, which specifically includes the following steps:

[0022] Step 1: Obtain the air quality parameters during the operation of the industrial air-conditioning system in the historical database, conduct in-depth analysis, and use the cluster analysis algorithm to divide the operating conditions of the industrial air-conditioning system to obtain the operating condition category;

[0023] Among them, indoor air quality parameters include but are not limited to: particle concentration (PM2.5, PM10), volatile organic compounds (VOCs) content;

[0024] Indoor air quality parameters are monitored and acquired through sensors of corresponding parameters. For example, the concentration of PM2.5 particles in the air is detected. Common PM2.5 sensor models include but are not limited to: PMS3003, PMS5003, PMS7003, PMS1003. The concentration of VOCs in the air is detected. Common VOCs sensor models include but are not limited to: SGP30, CCS811.

[0025] In some embodiments, data of air quality parameters during the operation of the industrial air conditioning system is obtained from an industrial air conditioning system operation database, and the obtained data is preprocessed using a set screening rule;

[0026] The set screening rules include but are not limited to: setting a time range and removing values ​​that exceed a reasonable range; the preprocessing process includes but is not limited to: cleaning and normalizing the data;

[0027] The pre-processed data is integrated to obtain a data set of Y air quality parameters during the operation of the industrial air conditioning system at X time. Can be used as a Y-dimensional vector;

[0028] It can be expressed as: , where 1≤i≤Y, X represents the time index, which is the time point at which the air quality parameter data is recorded, and Y represents the number of air quality parameters, that is, Y different air quality parameters are recorded at each time point;

[0029] The data of the data set should be in the form of an X*Y matrix, and the specific form is as follows:

[0030] ;

[0031] It should be explained that the representation of vectors and matrices plays an important role in data representation, storage, processing and algorithm input in this method. They make data processing more efficient and the logic clearer, and provide a solid foundation for subsequent clustering analysis and working condition division.

[0032] According to the characteristics of the data (multi-dimensional data, i.e., multiple air quality parameters) and clustering requirements (grouping similar working conditions together), the K-means clustering algorithm is selected for working condition division. The specific process is as follows:

[0033] The K-means algorithm is a segmentation-based clustering algorithm that is suitable for processing multidimensional data. It is relatively simple to understand and has high computational efficiency.

[0034] According to the distribution of data, different numbers of clusters (such as 2, 3, 4, etc.) are used, and the clustering effect is evaluated to obtain the number of clusters n;

[0035] Among them, the methods for evaluating the clustering effect include but are not limited to: calculating the similarity within the cluster (such as the sum of the distances from each point in the cluster to the cluster center) and the separation between clusters (such as the distance between cluster centers);

[0036] The integrated data set (including PM2.5 concentration, PM10 concentration, and VOCs content at each time point) is input into the K-means clustering algorithm;

[0037] The algorithm randomly selects n initial cluster centers, then calculates the distance from each data point to these n cluster centers, and assigns the data point to the cluster to which the nearest cluster center belongs;

[0038] Recalculate the cluster center of each cluster (i.e. the average value of all points in the cluster) and repeat the above steps until the cluster center no longer changes or the preset number of iterations is reached;

[0039] According to the clustering results, we divide the n clusters into n different working conditions;

[0040] Each working condition corresponds to the operating status and air quality parameter characteristics of the industrial air conditioning system under specific conditions;

[0041] In this example, for example, after trying different numbers of clusters, it was found that when the number of clusters was 3, the clustering effect was the best (i.e., the similarity within the cluster was high or the separation between the clusters was large), so the data was divided into 3 working conditions;

[0042] Working condition 1: The PM2.5 and PM10 concentrations are low, and the VOCs content is also low, indicating that the air quality is good and the system can operate at a lower power and air supply volume; Working condition 2: The PM2.5 and PM10 concentrations are high, but the VOCs content is low, indicating that there are more particulate matter in the air, and the system needs to strengthen air purification measures; Working condition 3: The VOCs content is high, while the PM2.5 and PM10 concentrations are relatively low, indicating that there are more volatile organic compounds in the air, and the system may need to increase ventilation or enable specific air purification equipment;

[0043] Step 2: Based on the divided operating condition zones of industrial air conditioners, real-time monitoring of air quality parameters is performed to identify the operating condition category of the air conditioning system;

[0044] In some embodiments, during the operation of the industrial air conditioning system, air quality parameters are monitored in real time, and the format of the air quality parameters is made consistent with the format of the input data of the clustering algorithm in step 1 through data processing;

[0045] Mapping the real-time monitored air quality parameter data to all operating condition categories, and respectively calculating the distance values ​​between the real-time monitored air quality parameter data and the operating condition cluster center, wherein the distance calculation method includes but is not limited to: Euclidean distance calculation method and Manhattan distance calculation method;

[0046] Extract the minimum value among all the distance values, and mark the operating condition corresponding to the minimum value among the distance values ​​as the real-time operating condition;

[0047] Exemplarily, the specific process of using the Euclidean distance calculation method to calculate the distance value between the real-time monitored air quality parameter data and the operating condition cluster center is as follows:

[0048] Euclidean distance The calculation formula is as follows: , where o is the real-time monitoring air quality parameter data, which is a P-dimensional vector. is the cluster center of the Kth operating condition, which is a P-dimensional vector. is the value of the real-time monitoring data point on the i-th air quality parameter, is the value of the cluster center of the kth operating condition on the i-th air quality parameter,

[0049] For the real-time monitored air quality parameter data, calculate the Euclidean distance values ​​with all K cluster centers to obtain a distance value vector: ;

[0050] By comparing the distance values ​​between the real-time monitored air quality parameter data and each cluster center, all corresponding operating conditions of the distance center with the smallest distance are selected as the real-time operating conditions;

[0051] The technical solution of this embodiment is: by obtaining the air quality parameters in the historical database, the operating conditions of the industrial air-conditioning system are divided by using the K-means clustering algorithm to obtain different operating condition categories, and then the air quality parameters are monitored in real time, and the real-time operating condition category of the air-conditioning system is identified by calculating the distance between the real-time monitoring data and the cluster center of each operating condition;

[0052] This enables the division and real-time monitoring of the operating conditions of industrial air-conditioning systems, helps to automatically adjust operating parameters according to current conditions, and achieve energy conservation and environmental protection goals. Through in-depth analysis of historical data and real-time monitoring data, this method can accurately reflect the operating status and air quality characteristics of the air-conditioning system under different conditions, providing strong support for the system's optimized control and energy-saving operation. Embodiment 2

[0053] Based on the above embodiments, Figure 1As shown, an embodiment of the present invention provides a diagnostic control method for energy-saving operation of an industrial air-conditioning system, which specifically includes the following steps:

[0054] Step 3: Based on the real-time operating conditions obtained, continuously track the air quality status and its changing trend over time, and identify and output normal or abnormal signals;

[0055] In some embodiments, based on obtaining the real-time operating conditions, a real-time parameter value matrix and a change rate value matrix are constructed for the air quality parameters under the real-time operating conditions, wherein the change rate value is the change rate of each air quality parameter at each time point relative to the previous time point;

[0056] The real-time parameter value matrix is: , the change rate value matrix is: ,in, , They respectively indicate that the value is located in the i-th row and j-th column of the corresponding matrix, A indicates the number of time points, and B indicates the number of air quality parameters;

[0057] At each time point, the real-time parameter value and the change rate value of each air quality parameter are weighted and summed to obtain the abnormal judgment value and construct the abnormal judgment value matrix: ,in, Indicates that the value is located in the i-th row and j-th column of the matrix. , s1 and s2 are weight coefficients, the value of s1 is 1.326, and the value of s2 is 1.174;

[0058] Set an abnormal judgment threshold, based on the value of each row in the abnormal judgment value matrix (i.e., the abnormal judgment value of any air quality parameter at each time point), compare it with the abnormal judgment threshold respectively. If the abnormal judgment value is greater than the abnormal judgment threshold, the corresponding abnormal judgment value is marked as an abnormal value; if the abnormal judgment value is less than or equal to the abnormal judgment threshold, the corresponding abnormal judgment value is marked as a normal value;

[0059] Count the number of abnormal values ​​and calculate the ratio with the total number of values ​​to get the abnormal number ratio;

[0060] The difference between the abnormal judgment value corresponding to the abnormal value and the abnormal judgment threshold is calculated to obtain the abnormal deviation value, the ratio of the abnormal deviation value to the abnormal judgment threshold is calculated to obtain the abnormal ratio, and all the abnormal ratios are summed and averaged to obtain the abnormal degree value;

[0061] The abnormal number ratio is multiplied by the abnormal degree value to obtain the abnormal characterization value;

[0062] Compare the abnormal characterization value with the abnormal characterization threshold, wherein the abnormal characterization threshold is set by a technician in this field based on historical multiple experimental data and corresponding real-time operating conditions;

[0063] If the abnormal characterization value is less than or equal to the abnormal characterization threshold, a normal signal is generated;

[0064] If the abnormal characterization value is greater than the abnormal characterization threshold, an abnormal signal is generated;

[0065] It should be explained that the above abnormal characterization value calculates the abnormal characterization value of any air quality parameter, that is, as long as any air quality parameter generates an abnormal signal, the system is in an abnormal state;

[0066] The technical solution of this embodiment is: based on the acquired real-time operating conditions, a real-time parameter value matrix and a change rate value matrix are further constructed, an abnormal judgment value is obtained by weighted sum calculation, and compared with the set abnormal judgment threshold, the abnormal quantity ratio and the abnormal degree value are comprehensively evaluated, the abnormal characterization value is calculated and compared with the abnormal characterization threshold, and a normal or abnormal signal is output;

[0067] This enables accurate identification of the operating status of the industrial air-conditioning system, timely discovery of abnormal conditions in the operation of the industrial air-conditioning system, improved fault detection capabilities, and a reliable basis for timely adoption of energy-saving and control measures, helping to ensure the stable operation of the system and energy-saving effects. Embodiment 3

[0068] Based on the above embodiments, Figure 1 As shown, an embodiment of the present invention provides a diagnostic control method for energy-saving operation of an industrial air-conditioning system, which specifically includes the following steps:

[0069] Step 4: Based on the abnormal signal, identify the specific abnormal air quality parameters, and optimize and adjust the operating parameters of the industrial air conditioner in combination with the real-time operating conditions;

[0070] In some embodiments, based on the abnormal signal, an air quality parameter corresponding to the generated abnormality is obtained, marked as an abnormal air quality parameter, and the marked abnormal air quality parameter is compared with the air quality parameter characteristics of the real-time operating condition to obtain a main control target;

[0071] For example, for abnormal air quality parameters PM2.5 and VOCs, the real-time operating condition is condition three: the VOCs content is high, while the PM2.5 concentration and PM10 concentration are relatively low, then the main control target is VOCs;

[0072] Based on the marked abnormal air quality parameters and combined with the adjustment strategy of real-time operating conditions, the air supply volume of industrial air conditioners is optimized and adjusted. The specific process is as follows:

[0073] The abnormal characterization value and the abnormal characterization threshold are calculated by difference, and the obtained difference is calculated by ratio with the abnormal characterization value to obtain the control coefficient;

[0074] The air supply control amount is calculated by multiplying the control coefficient by the current air supply amount;

[0075] Based on the correlation between the air supply volume and the current main control target, wherein the correlation between the air supply volume and the current main control target is obtained by calculating and analyzing the historical data combined with the correlation coefficient, and the correlation coefficient includes but is not limited to: Pearson correlation coefficient;

[0076] Take the absolute values ​​of all correlation coefficients to obtain the absolute correlation coefficient, extract the maximum absolute correlation coefficient, obtain the main control target corresponding to the maximum absolute correlation coefficient, and mark it as the priority control target;

[0077] It should be explained that if the absolute correlation coefficient maximum values ​​are tied, the experience and historical adjustment records of the technicians in this field are combined to select the priority control target;

[0078] If the air supply volume is positively correlated with the priority control target, the main control target will increase when the air supply volume increases. If the priority control target needs to be optimized, the difference between the current air supply volume and the air supply control amount is calculated to obtain the air supply control target value;

[0079] If the air supply volume is negatively correlated with the priority control target, the main control target will decrease when the air supply volume increases. If the priority control target needs to be optimized, the current air supply volume and the air supply control amount are summed to obtain the air supply control target value;

[0080] If the air volume is not related to the priority control target, other operating parameters are adjusted, including but not limited to: temperature, operating power of air purification equipment;

[0081] It is important to note that during the adjustment process, other parameters need to be monitored to ensure that they are not adversely changed by the increase in air supply;

[0082] The technical solution of this embodiment is: based on the abnormal signal, specific abnormal air quality parameters are identified, and combined with the real-time operating conditions, the air supply volume of the industrial air conditioner is optimized and adjusted by calculating the control coefficient and the air supply control amount, and based on the correlation between the air supply volume and the main control target (the priority control target is determined by analyzing historical data and correlation coefficients), if the correlation between the air supply volume and the priority control target is unclear, other operating parameters are adjusted;

[0083] In this way, air quality abnormalities can be detected quickly, and the operating parameters of industrial air conditioners, especially the air supply volume, can be effectively optimized through calculation and adjustment strategies to improve air quality in a targeted manner. At the same time, by comprehensively considering the correlation between air supply volume and multiple control objectives, the scientific nature and effectiveness of the adjustment strategy can be ensured, thereby improving the efficiency of air quality management, and also helping to save energy and reduce emissions and lower operating costs. Embodiment 4

[0084] Based on the above embodiments, Figure 2 As shown, an embodiment of the present invention provides a diagnostic control system for energy-saving operation of an industrial air-conditioning system, which specifically includes:

[0085] Operation condition category acquisition module: obtains the air quality parameters in the operation process of the industrial air-conditioning system in the historical database, conducts in-depth analysis, and uses the cluster analysis algorithm to divide the operation conditions of the industrial air-conditioning system to obtain the operation condition category;

[0086] Among them, indoor air quality parameters include but are not limited to: particle concentration (PM2.5, PM10), volatile organic compounds (VOCs) content;

[0087] Operating condition identification module: Based on the divided operating condition zones of industrial air conditioners, it monitors air quality parameters in real time and identifies the operating condition category of the air conditioning system;

[0088] Abnormal identification module: Based on the real-time operating conditions, it continuously tracks the air quality status and its changing trend over time, and identifies and outputs normal or abnormal signals;

[0089] Optimization and adjustment module: Based on abnormal signals, it identifies specific abnormal air quality parameters and performs optimization and adjustment in combination with real-time operating conditions. Embodiment 5

[0090] like Figure 3 As shown, an embodiment of the present invention further provides a computer device 3, comprising: a memory 302 and a processor 301 and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, a diagnostic control method for energy-saving operation of an industrial air-conditioning system as described in any one of the above methods is implemented.

[0091] The computer device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that

[0092] Figure 3It is only an example of computer device 3 and does not constitute a limitation on computer device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0093] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0094] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 3. Further, the memory 302 may also include both an internal storage unit and an external storage device of the computer device 3. The memory 302 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 302 may also be used to temporarily store data that has been output or is to be output. Embodiment 6

[0095] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements a diagnostic control method for energy-saving operation of an industrial air-conditioning system as described in any one of the above methods.

[0096] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0097] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0098] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0099] In the embodiments disclosed in the present application, it should be understood that the disclosed apparatus / terminal equipment and method can be implemented in other ways. For example, the apparatus / terminal equipment embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0100] One point, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, which may be electrical, mechanical or other forms.

[0101] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0103] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A diagnostic control method for energy-saving operation of an industrial air-conditioning system, characterized in that: The following steps are involved: Step 1: Obtain the air quality parameters during the operation of the industrial air-conditioning system in the historical database, conduct in-depth analysis, and use the cluster analysis algorithm to divide the operating conditions of the industrial air-conditioning system to obtain the operating condition category; Step 2: Based on the divided operating condition zones of industrial air conditioners, the air quality parameters are monitored in real time. By calculating the distance between the real-time monitoring data and the cluster center of each operating condition, the operating condition category of the air conditioning system is identified and the real-time operating condition is obtained. Step 3: Based on the real-time operating conditions obtained, continuously track the air quality status and its changing trend over time, and identify output abnormal signals; Step 4: Based on the abnormal signal, identify the specific abnormal air quality parameters, and optimize and adjust the industrial air conditioning operating parameters in combination with the adjustment strategy of the real-time operating conditions; The specific process of step 4 is as follows: Based on the abnormal signal, the air quality parameters corresponding to the generated abnormality are obtained and marked as abnormal air quality parameters. The marked abnormal air quality parameters are compared with the air quality parameter characteristics of the real-time operating conditions to obtain the main control targets; Based on the marked abnormal air quality parameters and combined with the adjustment strategy of real-time operating conditions, the air supply volume of industrial air conditioners is optimized and adjusted. The specific process is as follows: The abnormal characterization value and the abnormal characterization threshold are calculated by difference, and the obtained difference is calculated by ratio with the abnormal characterization value to obtain the control coefficient; The air supply control amount is calculated by multiplying the control coefficient by the current air supply amount; Based on the correlation between air supply volume and current main control target; Take the absolute values ​​of all correlation coefficients to obtain the absolute correlation coefficient, extract the maximum absolute correlation coefficient, obtain the main control target corresponding to the maximum absolute correlation coefficient, and mark it as the priority control target; If the air supply volume is positively correlated with the priority control target, the difference between the current air supply volume and the air supply control volume is calculated to obtain the air supply control target value; If the air supply volume is negatively correlated with the priority control target, the current air supply volume and the air supply control amount are summed to obtain the air supply control target value; If the air supply volume is not relevant to the priority control target, adjust other operating parameters.

2. A diagnostic control method for energy-saving operation of an industrial air conditioning system according to claim 1, characterized in that: The specific process of obtaining the operating condition category is as follows: Acquire the data of air quality parameters during the operation of the industrial air conditioning system from the industrial air conditioning system operation database, and pre-process the acquired data using the set screening rules; The pre-processed data is integrated to obtain a data set of Y air quality parameters during the operation of the industrial air conditioning system at X time. Can be used as a Y-dimensional vector; It can be expressed as: , where 1≤i≤Y, X represents the time index, which is the time point at which the air quality parameter data is recorded, and Y represents the number of air quality parameters, that is, Y different air quality parameters are recorded at each time point; The data of the data set should be in the form of an X*Y matrix, and the specific form is as follows: ； According to the characteristics of the data and clustering requirements, the K-means clustering algorithm is selected for working condition division. The specific process is as follows: According to the distribution of data, different numbers of clusters are used, and the clustering effect is evaluated to obtain the number of clusters n; Input the integrated data set into the K-means clustering algorithm; The algorithm randomly selects n initial cluster centers, calculates the distance from each data point to these n cluster centers, and assigns the data point to the cluster to which the nearest cluster center belongs; Recalculate the cluster center of each cluster and repeat the above steps until the cluster center no longer changes or the preset number of iterations is reached; According to the clustering results, the n clusters are divided into n different operating conditions, that is, n operating condition categories.

3. A diagnostic control method for energy-saving operation of an industrial air conditioning system according to claim 1, characterized in that: The process of identifying the operating condition category of the air conditioning system is as follows: During the operation of the industrial air conditioning system, air quality parameters are monitored in real time and data preprocessing is performed; Map the real-time monitored air quality parameter data to all operating condition categories, and calculate the distance value between the real-time monitored air quality parameter data and the operating condition cluster center; By comparing the distance values ​​between the real-time monitored air quality parameter data and each cluster center, all corresponding operating conditions of the distance center with the smallest distance are selected as the real-time operating conditions.

4. A diagnostic control method for energy-saving operation of an industrial air conditioning system according to claim 3, characterized in that: The process of calculating the distance between the real-time monitored air quality parameter data and the operating condition cluster center is as follows: The specific process of using the Euclidean distance calculation method to calculate the distance between the real-time monitored air quality parameter data and the operating condition cluster center is as follows: Euclidean distance The calculation formula is as follows: , where o is the real-time monitoring air quality parameter data, which is a P-dimensional vector. is the cluster center of the Kth operating condition, which is a P-dimensional vector. is the value of the real-time monitoring data point on the i-th air quality parameter, is the value of the cluster center of the kth operating condition on the i-th air quality parameter, For the real-time monitored air quality parameter data, calculate the Euclidean distance values ​​with all K cluster centers to obtain a distance value vector: .

5. A diagnostic control method for energy-saving operation of an industrial air conditioning system according to claim 1, characterized in that: The process of identifying the abnormal output signal is as follows: Obtaining an abnormal characterization value, and comparing the abnormal characterization value with an abnormal characterization threshold; If the abnormal characterization value is greater than the abnormal characterization threshold, an abnormal signal is generated.

6. A diagnostic control method for energy-saving operation of an industrial air conditioning system according to claim 5, characterized in that: The process of obtaining the abnormal characterization value is as follows: Obtain the abnormal judgment value and abnormal value, count the number of abnormal values, and calculate the ratio with the total number of values ​​to obtain the abnormal number ratio; The difference between the abnormal judgment value corresponding to the abnormal value and the abnormal judgment threshold is calculated to obtain the abnormal deviation value, the ratio of the abnormal deviation value to the abnormal judgment threshold is calculated to obtain the abnormal ratio, and all the abnormal ratios are summed and averaged to obtain the abnormal degree value; The abnormality representation value is obtained by multiplying the abnormality ratio by the abnormality degree value.

7. A diagnostic control method for energy-saving operation of an industrial air conditioning system according to claim 6, characterized in that: The process of obtaining the abnormal judgment value is as follows: Get real-time parameter value matrix and the matrix of change rate values ; At each time point, the real-time parameter value and the change rate value of each air quality parameter are weighted and summed to obtain the abnormal judgment value and construct the abnormal judgment value matrix: , where A represents the number of time points and B represents the number of air quality parameters. Indicates that the value is located in the i-th row and j-th column of the matrix. , s1 and s2 are weight coefficients.

8. A diagnostic control method for energy-saving operation of an industrial air conditioning system according to claim 7, characterized in that: The process of obtaining the real-time parameter value and the change rate value of the air quality parameter is as follows: Based on the real-time operating conditions, a real-time parameter value matrix and a change rate value matrix are constructed for the air quality parameters under the real-time operating conditions, wherein the change rate value is the change rate of each air quality parameter at each time point relative to the previous time point; The real-time parameter value matrix is: , the change rate value matrix is: ,in, , They respectively indicate that the value is located in the i-th row and j-th column of the corresponding matrix, A represents the number of time points, and B represents the number of air quality parameters.

9. A diagnostic control method for energy-saving operation of an industrial air conditioning system according to claim 7, characterized in that: The process of obtaining the abnormal value is as follows: Set the abnormal judgment threshold, obtain the abnormal judgment value matrix, and compare the value of each row in the abnormal judgment value matrix with the abnormal judgment threshold. If the abnormal judgment value is greater than the abnormal judgment threshold, mark the corresponding abnormal judgment value as an abnormal value.

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